English(EN) Scalable Logistic Gaussian Process Density Regression with Kinetic Langevin Sampling

新的逻辑高斯过程估计器使用动力学朗之万采样

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了一种新的可扩展的贝叶斯估计器，用于使用逻辑高斯过程进行条件密度估计。该方法采用动力学朗之万动力学对潜在场进行采样，提供了传统拉普拉斯或变分近似的替代方案。该估计器在具有数百万个训练观测值的照相红移基准测试中表现出竞争力，在密度和校准指标上取得了优异的成绩。 AI

影响引入了一种新颖的密度估计采样技术，有可能提高照相红移等应用中的性能。

排序理由该集群包含一篇详细介绍新统计方法及其应用的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Daniel Paulin, \'Ad\'am Jung, Andr\'as A. Bencz\'ur · 2026-10-08 04:00

具有动力学 Langevin 采样的可扩展逻辑斯蒂高斯过程密度回归

arXiv:2610.09591v1 Announce Type: cross Abstract: Conditional density estimation targets the full distribution of a response given covariates, as required, for example, for per-galaxy photometric redshifts. We develop a scalable Bayesian estimator based on the logistic Gaussian p…